1. Field
Embodiments of the present invention generally relate to maneuverability of robots, and in particular, to robots having self-righting capabilities.
2. Description of Related Art
Robots are increasingly being used for various applications including search and rescue operations, reconnaissance missions and combat situations. The use of robots in the field can improve combat effectiveness due to reduced mission completion times, increased mission success rates, and decreased casualty rates.
Unfortunately, during the performance of maneuvers, a robot may fall or tip over, preventing it from moving normally. Controlling the robot to successfully right itself can be a difficult and time-consuming task for operators, a major problem in situations that are already both time-sensitive and dangerous. For instance, those controlling the robot must determine how to re-orient the robot to a desired position, if it is even possible.
There have been several approaches employed, to-date, for robot self-righting, which can be categorized into four main groups: passive approaches, specific mechanisms, overturned drivability, and dynamic approaches. Passive approaches do not make an effort to actively self-right, relying on the shape of the robot and its center of mass location to allow for easy righting or to inhibit flipping. These approaches include geometries that possess some natural self-righting capability or preference for certain orientations, such as shaping the robot like a domed tortoise shell or an egg. By focusing on the geometry, little or no motion is needed to right the robot. Some robots rely on specific mechanisms for self-righting, such as a flipper. Another category of robots allow for upside-down operation, attempting to limit the need for self-righting. Other robots take a dynamic approach, focusing on the release of stored mechanical energy in an attempt to right the robot, such as by leveraging spring legs or generating rolling momentum.
Genetic and evolutionary algorithms for creating motion behaviors have also been proposed, which attempt to evaluate behaviors in physics simulations and simulate natural selection to achieve better control algorithms and minimize costs such as power consumed, the number of motions necessary, and the time elapsed in self-righting. It is not clear whether any of these has been actually implemented, and any solution achieved in this way is likely to be sub-optimal.
An improved robot self-righting methodology which could be applied to any generic robot would be useful.